Identification and analysis of adoption barriers of disruptive technologies in the logistics industry

• DOI: https://doi.org/10.1108/IJLM-07-2021-0352
• Published date: 30 June 2022
• Date: 30 June 2022
• Pages: 136-169
• Subject Matter: Management science & operations,Logistics
• Author: Bhawana Rathore,Rohit Gupta,Baidyanath Biswas,Abhishek Srivastava,Shubhi Gupta

Identification and analysis of

adoption barriers of disruptive

technologies in the

logistics industry

Bhawana Rathore

Institute of Business Management, GLA University, Mathura, India

Rohit Gupta

Operations Management Area, Indian Institute of Management Ranchi,

Ranchi, India

Baidyanath Biswas

Enterprise and Innovation Group, Dublin City University Business School,

Dublin, Ireland

Abhishek Srivastava

Operations Management and Decision Sciences,

Indian Institute of Management Kashipur, Uttarakhand, India, and

Shubhi Gupta

Organisational Behaviour andHuman Resource Area, FORE Schoolof Management,

New Delhi, India

Abstract

Purpose –Recently, disruptive technologies (DTs) have proposed several innovative applications in

managing logistics and promise to transform the entire logistics sectordrastically. Often, this transformation is

not successful due to the existence of adoption barriers to DTs. This study aims to identify the significant

barriers that impede the successful adoption of DTs in the logistics sector and examine the interrelationships

amongst them.

Design/methodology/approach –Initially, 12 critical barriers were identified through an extensive

literature review on disruptive logistics management, and the barriers were screened to ten relevant barriers

with the help of Fuzzy Delphi Method (FDM). Further, an Interpretive Structural Modelling (ISM) approach was

built with the inputs from logistics experts working in the various departments of warehouses, inventory

control, transportation, freight management and customer service management. ISM approach was then used

to generate and examine the interrelationships amongst the critical barriers. Matrics d’Impacts Croises-

Multiplication Applique a Classement (MICMAC) analysed the barriers based on the barriers’driving and

dependence power.

Findings –Results from the ISM-based technique reveal that the lack of top management support (B6) was a

critical barrier that can influence the adoption of DTs. Other significant barriers, such as legal and regulatory

frameworks (B1), infrastructure (B3) and resistance to change (B2), were identified as the driving barriers, and

IJLM

33,5

136

© Bhawana Rathore, Rohit Gupta, Baidyanath Biswas, Abhishek Srivastava and Shubhi Gupta.

Published by Emerald Publishing Limited. This article is published under the Creative Commons

Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works

of this article (for both commercial and non-commercial purposes), subject to full attribution to the

original publication and authors. The full terms of this licence may be seen at http://creativecommons.

org/licences/by/4.0/legalcode

The authors gratefully acknowledge the reviewers for the constructive comments that led to the

significant improvements to this manuscript. Shubhi Gupta acknowledges the infrastructural support

received from FORE School of Management, New Delhi in completing this paper.

The current issue and full text archive of this journal is available on Emerald Insight at:

https://www.emerald.com/insight/0957-4093.htm

Received 1 July 2021

Revised 16 January 2022

4 April 2022

Accepted 17 May 2022

The International Journal of

Logistics Management

Vol. 33 No. 5, 2022

pp. 136-169

Emerald Publishing Limited

0957-4093

DOI 10.1108/IJLM-07-2021-0352

industries need to pay more attention to them for the successful adoption of DTs in logistics. The MICMAC

analysis shows that the legal and regulatory framework and lack of top management support have the highest

driving powers. In contrast, lack of trust, reliability and privacy/security emerge as barriers with high

dependence powers.

Research limitations/implications –The authors’study has several implications in the light of DT

substitution. First, this study successfully analyses the seven DTs using Adner and Kapoor’s framework

(2016a, b) and the Theory of Disruptive Innovation (Christensen, 1997; Christensen et al., 2011) based on the two

parameters as follows: emergence challenge of new technology and extension opportunity of old technology.

Second, this study categorises these seven DTs into four quadrants from the framework. Third, this study

proposes the recommended paths that DTs might want to follow to be adopted quickly.

Practical implications –The authors’study has several managerial implications in light of the adoption of

DTs. First, the authors’study identified no autonomous barriers to adopting DTs. Second, other barriers

belonging to any lower level of the ISM model can influence the dependent barriers. Third, the linkage barriers

are unstable, and any preventive action involving linkage barriers would subsequently affect linkage barriers

and other barriers. Fourth, the independent barriers have high influencing powers over other barriers.

Originality/value –The contributions of this study are four-fold. First, the study identifies the different DTs

in the logistics sector. Second, the study applies the theory of disruptive innovations and the ecosystems

framework to rationalise the choice of these seven DTs. Third, the study identifies and critically assesses the

barriers to the successful adoption of these DTs through a strategic evaluation procedure with the help of a

framework built with inputs from logistics experts. Fourth, the study recognises DTs adoption barriers in

logistics management and provides a foundation for future research to eliminate those barriers.

Keywords Disruptive technologies, Internet of things, Blockchain, Bigdata, Drone, Driverless vehicle,

Artificial intelligence, 3D printing, Logistics management

Paper type Research paper

1. Introduction and motivation

Disruptive innovation has influenced the logistics industry, with most firms attempting to

adapt to a rapidly changing environment. Many organisations are transforming their

logistics networks to remain competitive and sustainable in the continuously evolving

technological environment (Winkelhaus and Groose, 2020). For instance, Kouvolo Innovation,

a Finnish company, has collaborated with International Business Machines (IBM) to build a

blockchain-based system for shipping containers (Del Castillo, 2017). Recently, major

European operators have joined Tradelens to enable information-sharing across diverse

supply chains (SCs), increase industry innovation, reduce trade friction and endorse more

global trade [1]. Although experts expect that blockchains will deliver significant benefits

(Hughes et al., 2019), freight logistics firms prefer to operate with simpler technologies rather

than adopt more advanced ones (Janjevic et al., 2019).

With more focus on the Internet of Things (IoT), logistics firms and SCs immensely benefit

from IoT adoption. IoTs are expected to generate US$1.9 trillion in economic value globally

across the SCs and logistics sectors [2]. Further, according to DHL, IoTs help track shipments,

manage warehouse inventory and optimise vehicle fleets. Recently, Saia LTL Freight [2]

incorporated Intel’s IoT on its truck fleets to track maintenance schedules, the health and

safety of the drivers and the frequency of refuelling. However, IoT adoption in the logistics

sector is not without challenges. A critical challenge is maintaining the consistency between

the centralised information technology (IT) records and data feeds from the installed IoT

sensors (Tu, 2018). Further, a big challenge with IoT adoption is the highly uncertainfinancial

returns on technology investment [3]. Finally, realising the full potential of IoTs may require

significant managerial attention for handling issues such as analytics challenges and

cybersecurity [4].

As firms focus more on improving the operational efficiency of their SCs and logistics,

they are opting for automation technologies more frequently, such as drone-based delivery.

For instance, Swiss Post and Matternet are conducting trial drone-based deliveries [5].

Recently, Aha has been delivering food items and small consumer goods with the help of

drones within a limited radius of 2.5 miles [6]. However, there are obstacles (such as poor

Disruptive

technologies’

adoption

barriers

137

weather,other flying objects and drone loudness) that current market players such as Amazon

face whilst serving drone-based deliveries [7]. Further, drone-based deliveries in densely

populated urban areas are too risky [8]. Whilst many research projects have proven the

success of drone-based deliveries, in reality, infrastructural support and regulatory factors

can determine its commercial viability [9,10].

Global logistics firms are also expected to increase their digital technologies, such as

artificial intelligence (AI) to meet the growing e-commerce demand [11]. Traditional logistics

and transportation firms still depend heavily on human labour for logistical processes [12],

which can improve with the adoption of AI. Many logistics firms use robots and AI-based

mechanical arms to reduce human intervention in logistical operations. For instance, XPO

Logistics, Rakuten and JD.com are using AI-based robots for delivering the goods ordered by

customers [13,14]. However, these firms face problems due to the enormous range of items

these robots need to lift and carry in the warehouses [13]. In another instance, KNAPP AG, the

Austrian logistics firm, reported that AI-based robots could successfully handle only about

15% of all items [15]. Further, most of these robots could not grip soft objects properly,

leading to inefficient usage of AI-based technologies [16].

The current competitive environment in the logistics industry leads to a huge increase in

business data. Big Data Analytics can be a solution to handle these challenges and provide a

competitive advantage to logistics firms. For instance, service delivery time can be optimised

through advanced predictive techniques. DHL Smart Trucks operate on real-time

geographical, traffic and weather data to plan the delivery routes dynamically. Big Data

Analytics can also provide a versatile platform to create valuable customer insights and

recommendations built from existing data, customer feedback and demographics to improve

delivery [17]. However, the implementation of Big Data projects is not without its challenges.

First, a strong alignment between business units and the IT departments must be maintained

(Bean and Davenport, 2019;Wamba et al., 2018). Second, organisational data must be

accessible to all stakeholders [17]. Third, organisations need to hire data scientists to manage

these projects efficiently [18].

Many logistics firms and organisations plan to adopt autonomous vehicles (AVs) to ease

transportation hurdles. For instance, TuSimple [19] collaborates with major Third-party

logistics (3PL) operators to improve freight delivery with AVs [20]. Next, Amazon plans to

adopt AVs to overcome logistical challenges [21]. However, opting for AVs is costlier for

logistics firms, and they need regular maintenance [22]. AVs have inadequate scope in the

trucking industry due to their obvious disadvantage whilst long-distance driving on

highways [23]. Besides, the global adoption of AVs in the logistics sector has safety concerns.

For instance, Uber’s autonomous car was involved in a fatal accident [24], whilst the image-

processing algorithms of AVs could not identify objects as accurately as predicted [25].

Next, there is growing hype and excitement about 3D printing (or additive manufacturing)

technologies that can potentially revolutionise the logistics sectors. For instance, Amazon has

designed delivery trucks fitted with 3D printers to manufacture products on the way to a

customer destination. Therefore, it can drastically reduce the lead time of customised delivery

[26]. However, many challenges prevent the successful adoption of 3D printing in the logistics

sector. For instance, Ford Motors is adopting 3D printers to mass produce spare parts.

However, the production speed of these 3D printers is much lower than the traditional

machines, leading to a higher lead time. Again, 3D printing is a fast-developing technology,

and organisations fear that their initial investments will be obsolete within the next few years.

Thus, the feasibility of 3D printing remains a significant challenge.

Therefore, firms must identify various barriers before considering the implementation of

disruptive technologies (DTs) (Christensen, 2013;Rogers et al., 2016;Zhong et al., 2016;

Hofmann and R€

usch, 2017;Kim et al., 2017;Hopkins and Hawking, 2018;McDonald, 2019;Sah

et al., 2021). Thus, in this study, (1) we list the barriers, (2) select the relevant barriers with the

IJLM

33,5

138